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Fusing point and areal level space–time data with application to wet deposition
Author(s) -
Sahu Sujit K.,
Gelfand Alan E.,
Holland David M.
Publication year - 2010
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/j.1467-9876.2009.00685.x
Subject(s) - context (archaeology) , deposition (geology) , interpolation (computer graphics) , grid , environmental science , bayesian inference , data set , meteorology , computer science , data mining , bayesian probability , geography , geology , artificial intelligence , motion (physics) , paleontology , archaeology , geodesy , sediment
Summary. Motivated by the problem of predicting chemical deposition in eastern USA at weekly, seasonal and annual scales, the paper develops a framework for joint modelling of point‐ and grid‐referenced spatiotemporal data in this context. The hierarchical model proposed can provide accurate spatial interpolation and temporal aggregation by combining information from observed point‐referenced monitoring data and gridded output from a numerical simulation model known as the ‘community multi‐scale air quality model’. The technique avoids the change‐of‐support problem which arises in other hierarchical models for data fusion settings to combine point‐ and grid‐referenced data. The hierarchical space–time model is fitted to weekly wet sulphate and nitrate deposition data over eastern USA. The model is validated with set‐aside data from a number of monitoring sites. Predictive Bayesian methods are developed and illustrated for inference on aggregated summaries such as quarterly and annual sulphate and nitrate deposition maps. The highest wet sulphate deposition occurs near major emissions sources such as fossil‐fuelled power plants whereas lower values occur near background monitoring sites.